What exactly is AiVIS Cite Ledger?

AiVIS Cite Ledger is an evidence-backed AI citation audit and remediation system. It measures how AI models, ChatGPT, Perplexity, Claude, and Google AI, interpret, trust, and cite your web content. The result is a 0–100 CITE LEDGER score across seven weighted dimensions, with BRAG evidence-linked findings and prioritized fixes.

How is this different from Semrush, Ahrefs, or Clearscope?

Traditional SEO tools optimize for keyword ranking in traditional search results. AiVIS Cite Ledger audits citation readiness, the specific signals that determine whether AI answer engines retrieve and cite your content. A page can rank #1 on Google and be completely invisible to AI answer synthesis.

What is BRAG and how does it make findings trustworthy?

BRAG stands for Based-Retrieval-Auditable-Grading. It ties every audit finding to a real, traceable element on your page, a specific heading, schema block, meta tag, or content section, each carrying a unique evidence ID. You can open the element, verify the finding, and recheck it after a fix. No black boxes.

Is the free tier genuinely useful, or just a teaser?

The Observer (free) plan gives you a full composite CITE LEDGER score and all seven dimension letter grades, so you can see exactly where you stand and which categories are hurting you. BRAG evidence IDs and prioritized fix packs require a paid plan. Three audits per month, no credit card.

How to Fix AI Search Visibility, Step-by-Step | AiVIS Cite Ledger

AI search visibility is not about keywords or backlinks. It is about whether AI models can structurally extract, trust, and cite your content. Here is how to fix it.

Step 1: Audit your current extraction readiness

Before fixing anything, you need a baseline. Run an AiVIS Cite Ledger audit to see exactly how AI models interpret your site right now. The audit scores seven weighted dimensions: schema & structured data, content depth, technical trust, meta tags & Open Graph, AI readability, heading structure, and security & trust.

Each dimension targets a specific failure mode in the AI extraction pipeline. Your lowest-scoring dimensions reveal the highest-impact fixes.

Step 2: Fix access and technical blockers

Check robots.txt for GPTBot, ClaudeBot, and PerplexityBot blocks. Verify server-side rendering is enabled. Confirm HTTPS is enforced and canonical URLs are set. These are floor-level requirements, if AI crawlers cannot access your content, nothing else matters.

Step 3: Add structured data and entity signals

Add JSON-LD schema: Organization (establishes entity identity), Article or BlogPosting (for content pages), FAQ (for Q&A content), and BreadcrumbList (for hierarchy). Ensure datePublished, author, and publisher properties are present and accurate.

Structured data is the primary mechanism AI models use for entity disambiguation. Without it, your content may be attributed to a competitor or to no source at all.

Step 4: Re-audit and track improvement

After implementing fixes, re-audit to measure the delta. AiVIS Cite Ledger stores your previous baseline, so you can see exactly which issues were resolved, which regressed, and what score improvement resulted from each change.

Frequently Asked Questions

How quickly will fixes improve AI visibility?: Most structural fixes (schema, robots.txt, SSR) take effect within 2–4 weeks as AI crawlers re-index. Content improvements may take longer depending on crawl frequency.
Do I need to redesign my site?: Rarely. Most AI extraction fixes are configuration changes: updating robots.txt, adding JSON-LD, enabling SSR. No visual redesign is typically required.